Dataset Information

Assessment of inter-individual, geographic, and seasonal variability in estimated human exposure to fine particles.

ABSTRACT: Health effects associated with ambient fine particle (PM(2.5)) exposure are typically estimated based on concentration-response (C-R) functions using area-wide concentration as an exposure surrogate. Persons 65 and older are particularly susceptible to adverse effects from PM(2.5) exposure. Using a stochastic microenvironmental simulation model, distributions of daily PM(2.5) exposures were estimated based on ambient concentration, air exchange rate, penetration factor, deposition rate, indoor emission sources, census data, and activity diary data, and compared for selected regions and seasons. Even though the selected subpopulation spends an average of over 20 h per day indoors, the ratio of daily average estimated exposure to ambient concentration (E(a)/C) is approximately 0.5. The daily average E(a)/C ratio varies by a factor of 4-5 over a 95% frequency range among individuals, primarily from variability in air exchange rates. The mean E(a)/C varies by 6-36% among selected NC, TX, and NYC domains, and 15-34% among four seasons, as a result of regional differences in housing stock and seasonal differences in air exchange rates. Variability in E(a)/C is a key factor that may help explain heterogeneity in C-R functions across cities and seasons. Priorities for improving exposure estimates are discussed.

SUBMITTER: Jiao W

PROVIDER: S-EPMC3658126 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Assessment of inter-individual, geographic, and seasonal variability in estimated human exposure to fine particles.

Jiao Wan W Frey H Christopher HC Cao Ye Y

Environmental science & technology 20121107 22

Health effects associated with ambient fine particle (PM(2.5)) exposure are typically estimated based on concentration-response (C-R) functions using area-wide concentration as an exposure surrogate. Persons 65 and older are particularly susceptible to adverse effects from PM(2.5) exposure. Using a stochastic microenvironmental simulation model, distributions of daily PM(2.5) exposures were estimated based on ambient concentration, air exchange rate, penetration factor, deposition rate, indoor e ...[more]

PMID: 23095102

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Project description:PurposeInter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.MethodsPBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.ResultsConsidering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.ConclusionsThe presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Dataset Information

Assessment of inter-individual, geographic, and seasonal variability in estimated human exposure to fine particles.

Publications

Assessment of inter-individual, geographic, and seasonal variability in estimated human exposure to fine particles.

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